When any digital image is displayed on some output medium (e.g., film or a CRT monitor), its image quality depends on a number of important factors, such as the characteristics of the device used to acquire the original data, the capabilities of the output device (dynamic range, noise characteristics, sharpness, etc.), and the image processing that has been performed on the image prior to display. In particular, the image processing tone reproduction function (i.e., the tonescale or gradation curve) used to map the digital values of the image into viewable shades of gray is a key component in producing a useful output image.
In the case of diagnostic radiology, for example, the tonescale used for display of images of various body parts has a significant impact on the ability of the image interpreter (i.e., the radiologist or physician) to extract useful diagnostic information from the image. In conventional (analog) screen/film radiography, this output tonescale is generally already designed into the film by the film manufacturer, and a variety of different films is available to get different `looks` depending on exam types, exposing techniques, and observer preferences. The ability in digital radiography systems to adjust this output tonescale in the computer before the final image is displayed is one of the most powerful features of such systems. However, choosing or creating the appropriate tonescale is not always a simple operation because it depends, among other things, on the above mentioned factors (exam type, exposure conditions, imaging modality, dynamic range of the output device, etc.).
Ideally each digital image should have its own tonescale function in order to be displayed optimally. This is because the actual distribution of gray levels in an image (i.e., the image histogram) is different for each image. Usually, however, there are some general similarities within certain classes of images (e.g., for a given x-ray acquisition device and set of exposure conditions, all adult chest radiographs will probably share some common characteristics) that make the tonescale generation process somewhat easier. The tonescale transformation that produces an image of high diagnostic quality must ensure, among other things, good contrast in the region of interest, reasonable contrast in the remainder of the image, no clipping (i.e. saturation) of useful anatomical detail at the ends of the gray level range, and no artifacts.
An additional complication in generating an optimum output tonescale for a given digital image, already mentioned briefly above, is that this tonescale depends critically on how the image was acquired. The creation of a digital image requires an analog to digital (A/D) conversion process in which the values of the continuously varying physical variable being measured (for example, x-ray transmittance or attenuation, proton density, or radioactivity) are transformed (quantized) into the set of discrete gray levels that represent the image in the digital domain. This A/D conversion function, also known as the input calibration function, is generally a monotonic nonincreasing or nondecreasing function. It assigns to each value of the physical variable a gray level in the digital image. Thus, the output tonescale, which maps these input gray levels to output gray levels for a display device, is essentially a mapping of the values of the input physical variable into output luminance on a display (e.g. a CRT or film on a viewbox). At the same time, small differences in gray level in the digital image are related to small differences in the value of the physical variable (through the derivative of the input calibration function). Thus, the shape (specifically, the local slope) of the output tonescale also determines the visual contrast of small differences in the input physical variable.
Two commonly used forms of input calibration function are linear and logarithmic transformations, although there are also variety of other nonlinear transformations. In the linear transformation, the digital gray levels are lineally related to values of the input physical variable: EQU m.sub.i =.alpha..sup.* x+.beta.,
where the m.sub.i are the input gray levels, x is the physical variable being measured, and .alpha. and .beta. are constants. In the case of logarithmic conversion, the input gray levels vary logarithmically with the input physical variable: EQU m.sub.i =.sigma..sup.* log(x+.gamma.)+.delta.
where m.sub.i and x are as above and .sigma., .gamma., and .delta. are constants. Tonescale mapping functions designed for logarithmically acquired input images will produce suboptimal results when applied to linearly acquired images and vice versa. This is because the human visual system will perceive a different visual contrast of various structures in the output image depending on whether small differences in gray level are linearly or logarithmically related to small differences in the physical variable being displayed. Thus, in order to be adaptive and robust, a tonescale generation method must be able to produce high-quality results with different kinds of input calibration functions.
Various methods have been tried to generate tonescale curves for digital images, and especially digital radiographic images. One common technique is called histogram equalization (see, for example, Castleman, Digital Image Processing). In this technique, an attempt is made to transform the input image into an output image that has a uniform (i.e., flat) histogram over the entire output gray level range, the idea being that this output image makes maximal use of the available gray levels, and has, according to information theory, maximum information. In most cases, histogram equalization is a fairly severe transformation. Highly populated regions of the input histogram are spread over a wider range of gray levels on output, which increases their visual contrast and is usually desirable, if not overdone. However, sparsely populated regions of the histogram (usually the extremes) are compressed into a smaller gray level range, which decreases visual contrast and can obscure important diagnostic details. In addition, the presence of nonuniform foreground and background regions in an image (e.g., collimator blades, direct x-ray transmission outside the patient) can cause this technique to waste valuable output gray levels on diagnostically unimportant regions of the image.
Other techniques, instead of using locally adaptive transformations as above, have used gross statistical features (variance, skewness, percentiles, etc.) of the input histogram in order to calculate the tonescale. This approach does not always produce acceptable contrast and can also produce unwanted clipping at the ends of the gray level range. European Patent Application EP283255 teaches such a technique in which operator intervention is needed to set the initial tonescale parameters for a series of similar images to get a reasonable contrast and to avoid clipping, after which the tonescales are calculated automatically based on the mean and variance of each input histogram.
Another method has been disclosed in U.S. Pat. No. 4,302,672 in which an optimal tonescale transformation is derived for a PA (postero-anterior) or AP (antero-posterior) chest radiograph generated on a storage phosphor acquisition system (with a logarithmic input calibration function). This patent teaches that by identifying in the input image histogram the spine, heart and lung regions, and assigning the appropriate contrast to each in the output tonescale, an improved output image can be gotten. The lung region gets the highest contrast, the heart a somewhat lower contrast, and the spine gets the lowest contrast. While this approach may work for certain kinds of chest images, it may fail in chests where it is not possible to identify the three structures correctly (e.g., in cases where the lungs are fluid-filled and have comparable density to the heart or spine). Furthermore, a similar analysis of other exam types with the appropriate anatomical landmarks would need to be done so that the method could work for all diagnostic applications. In addition, this method is not robust with respect to other input calibration functions, since the appropriate contrasts for spine, heart and lungs will be different depending on the A/D conversion technique.
As an alternative to this approach, U.S. Pat. No. 4,641,267 teaches a method, again for computed radiography, that is based on the use of only a few reference tonescale functions. In order to generate the actual tonescale for a particular input image, one of the reference tonescales is selected (depending on the body part) and this function is shifted and rotated by varying amounts depending on the exposure and other parameters of the image. While this method avoids generating and storing a large number of tonescales for varying conditions a priori, it (a) is not completely automatic and adaptive, since only shifting and rotating of a fixed set of predetermined curves is allowed, (b) is not robust with respect to different digital imaging modalities or input calibration functions.
Another method has been proposed in U.S. Pat. No. 5,046,118 (Ajewole and Schaetzing) that uses some local adaptivity and some global constraints to produce a custom tonescale for each input image. This method uses the entropy of subsets of the complete image to divide the image into a region of interest (containing anatomical information) and a background region, and then constructs separate tonescales for each of these regions based on global luminance and contrast constraints. The tonescale in the region of interest is substantially linear (assuming a linear input calibration function), while the tonescale in the background region is nonlinear in order to reduce the contrast of unimportant parts of the image. The two partial tonescales are subject to certain matching criteria at their boundary in order to produce a smooth final output. While this method is fairly robust with respect to histogram shape and varying amounts of extraneous background, it does not adapt well to differences in input calibration function, and thus will produce suboptimal tonescales for other than linear A/D conversions.
There is presently a need for an automatic method and apparatus for generating tonescale transformation functions that are robust with respect to image-to-image variations, imaging modalities, exposure conditions and input calibration functions.